Cautious classification with data missing not at random using generative random forests

DOI10.1007/978-3-030-86772-0_21OpenAlexW3199651480MaRDI QIDQ2146029

Julissa Villanueva Llerena, Denis Deratani Mauá, Alessandro Antonucci

Publication date: 15 June 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-86772-0_21

zbMATH Keywords

missing dataprobabilistic circuitsconservative inference rulegenerative random forests

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Cites Work

• Decision-theoretic specification of credal networks: a unified language for uncertain modeling with sets of Bayesian networks
• Robustifying sum-product networks
• Conditional sum-product networks: modular probabilistic circuits via gate functions
• Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks
• Evaluating credal classifiers by utility-discounted predictive accuracy
• Partial identification with missing data: concepts and findings
• Conservative Inference Rule for Uncertain Reasoning under Incompleteness
• Inference and missing data
• Probabilistic Inference in Credal Networks: New Complexity Results

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